Papers

Over the past decade, semiconductor spin qubits have progressed from few-qubit demonstrations towards larger-scale devices fabricated in increasingly reproducible academic and industrial processes. This progress marks an inflection point: the central challenge is no longer to demonstrate high-fidelity operation in carefully tuned devices, but to discover, verify, and maintain stable operating conditions reliably across many interdependent controls, varied device geometries, and disparate material platforms. In this Perspective, we frame spin-qubit operation as a modular automation problem. We decompose the workflow into five modules: bootstrapping from minimal prior information, configuration tuning, virtualization of physical gates into effective control axes, qubit-level tuning, and an operation layer with drift-aware maintenance. Using recent demonstrations from our work and the broader community, we argue that scalability will depend on explicit interfaces between modules, standardized intermediate data products, and workflow-level metrics such as throughput, success probability, stability time, recovery time, and robustness. We close by outlining the infrastructure needed to move beyond isolated tuning demonstrations toward sustained operation: qubit-performance-aware feedback, reusable software and benchmark tasks, and tight collaboration among experimental, theoretical, and software efforts.

In this paper, using a simplified model of the xenon atom, we numerically study the possibilities of efficient generation of coherent pulses in the XUV range through the resonant interaction of atoms with a moderate-intensity laser field, leading to the generation of its harmonics. We demonstrate the interplay of two systems of resonances affecting the harmonic generation efficiency. One is the channel-closing resonances, which arise when the sum of ionization and ponderomotive energies is equal to the energy of an integer number of laser photons. The second is the bound-bound transition resonances corresponding to an integer number of photons with a total energy equal to the energy gap between the Stark-shifted ground and excited states. The harmonic yields in the range of laser parameter values where both resonances occur exhibit a peculiar behavior, namely, near the intersection point of the resonances, a pronounced dip is observed, while the regions of increased generation efficiency due to the combined contribution of both enhancement mechanisms are slightly shifted from this point. We argue that this behavior, which is somewhat similar to the well-known phenomenon of 'avoided crossings', is associated with the formation of Fano-type resonant spectral lines. In contrast to 'avoided crossing' phenomena known in molecular physics, in the found interplay the contribution of one resonance system can be controlled, which is useful for experiments.

The $5S_{1/2} \rightarrow 6P_{3/2}$ transition of rubidium at $420$ nm is a promising candidate for a portable warm-vapor all-optical atomic clock. Despite recent precision spectroscopy studies at $420$ nm in Rb, an experimental determination of the saturation intensity of this transition has not yet been reported. The saturation intensity is a fundamental parameter that influences the identification of a potential clock transition frequency in terms of optimizing various intensity-dependent parameters and connected systematics. In this work, we report the first experimental measurement of the saturation intensity of the $420$ nm transition in Rb, obtaining $(23.18 \pm 0.28)$ mW/cm$^2$ for the $^{87}$Rb $F = 2 \rightarrow F' = 3$ transition and $(25.56 \pm 0.37)$ mW/cm$^2$ for the $^{85}$Rb $F = 3 \rightarrow F' = 4$ transition, in excellent agreement with theoretical predictions. We further investigate the temperature dependence of the Doppler-free Lamb-dip amplitude and linewidth over $59.03~\pm~0.37$ - $91.20~\pm~0.90^\circ$C in a $100$ mm commercial vapor cell, identifying around $82.02~\pm~ 0.73^\circ$C as the optimal operating temperature, where the signal-to-noise ratio of the Lamb-dip amplitude with temperature reaches a maximum and the observed Lamb-dip linewidth exhibits a minimum. We also present precise measurements of the magnetic-dipole ($A$) and electric-quadrupole ($B$) hyperfine constants of the $6P_{3/2}$ state for both isotopes, with the measured values being consistent with previously reported values for the hyperfine constants.

We investigate the formation of a barrier to evaporation that develops when levitated nanoscale Au nanoparticles are exposed to pulses of 532 nm laser radiation in a high vacuum (pressure $p=10^{-8}-10^{-7}$ Torr) environment. Our data are derived from precision measurements of the charge to mass ratio ($Q/M$) of $\sim$200 nm diameter Au particles confined in a quadrupole ion trap. We characterize the development of the barrier over time as the particle is repeatedly heated with laser pulses and determine the impact of variations of the interval between pulses and of exposure to several gases added to the vacuum chamber. We observe a slow increase in the mass of particles upon prolonged exposure to the vacuum, which we attribute to the growth of a barrier layer. For particles that have acquired a barrier during exposure to CO, we observe a rapid decrease in their mass upon subsequent exposure to O$_2$. These findings are consistent with the growth and subsequent oxidation of a graphene layer on the Au that forms the barrier to evaporation. However, we have not found that the rate of formation of the barrier depends on the pressure of carbon-containing gases (CO, C$_2$H$_4$, CO$_2$) we have added to the chamber. We hypothesize that a rare surface state on the solid Au particle catalyzes the reaction that introduces C to the particle. Repeated laser pulse heating is necessary–either to enable diffusion away from this state or to create fresh states that allow continued C uptake–to facilitate the growth of the surface graphene layer.

Magnonic systems, which exploit spin-wave excitations in magnetic materials, offer a promising platform for coherent information processing due to their low dissipation, strong nonlinearities, and intrinsic nonreciprocity. However, scaling magnonic circuits remains challenging, particularly with low-loss insulators such as yttrium iron garnet (YIG), which are difficult to pattern. Here, we experimentally realize a synthetic dimension in a magnonic system by coupling multimode magnon resonances in the frequency domain using time-periodic Floquet modulation. This approach enables electronically tunable interactions between discrete modes within a single YIG device, forming a reconfigurable mode-space lattice that supports functionalities such as Bloch oscillation. Our results demonstrate that high-dimensional magnonic dynamics can be achieved without increasing device footprint, establishing synthetic dimensions as a scalable and programmable route for integrated magnonic technologies. This advancement positions magnonic systems as promising platforms for engineering emergent phenomena that are inaccessible at equilibrium.

Wind shear and wind veer (gradients of wind speed and direction, respectively) are ubiquitous in the atmospheric boundary layer (ABL), and wind turbines therefore routinely operate in sheared and veered conditions. Previous field campaigns have observed statistically significant variations in power production efficiency (quantified by a power coefficient) upwards of 15% due to shear and veer. However, it is not yet clear how non-uniform inflow conditions alter rotor aerodynamics and drive these efficiency variations. In this study, we perform concurrent-precursor large-eddy simulations (LES) of an actuator disk-modeled wind turbine across stratified ABL conditions to demonstrate that shear and veer can reduce wind power efficiency by more than 20%. To support these ABL simulations, we perform simplified inflow LES where shear and veer are controlled independently. Using these controlled simulations, we demonstrate that shear and veer effects can be decomposed into: (1) geometric effects, due to changes in rotor-equivalent wind speed, and (2) inductive effects, which change the rotor aerodynamics and induced velocities. Inductive effects of wind shear modulate the power coefficient through changes to the local induction, while inductive effects of wind veer reduce the power coefficient by generating an adverse pressure gradient at the rotor scale. The geometric and inductive effects of shear and veer approximately linearly superimpose, with increasing losses as shear and veer magnitudes increase. Inductive effects account for a significant fraction of the observed losses, and the induction of a turbine is affected by shear, veer, and wall proximity through processes that are neglected in existing engineering models. Revealing the mechanisms through which shear and veer affect rotor performance establishes a framework that can enable improved power prediction in realistic ABL conditions.

This work presents a new numerical code for solving the time–dependent Gross–Pitaevskii–Poisson (GPP) system using adaptive mesh refinement (AMR). The code is designed to study the nonlinear dynamics of self–gravitating bosonic matter in three spatial dimensions under periodic boundary conditions. It combines high–order spatial discretization, explicit time integration, and dynamic refinement driven by the magnitude of the gravitational potential. The implementation is validated through a set of test problems in the nonlinear regime. These benchmarks demonstrate that the solver accurately preserves global conservation laws, resolves strong wave interference and phase singularities, and maintains consistency across refinement levels in highly dynamical scenarios.

We derive the exact image-dipole potential of a point dipole inside a closed cuboidal superconducting trap. The construction generalises the parallel-plate result to a geometry that confines every translational degree of freedom, and we prove that the image lattice satisfies the Meissner boundary condition on all six walls. For a centred dipole the orientational energy reduces to a diagonal quadratic form whose three coefficients are Epstein-zeta-type lattice sums. We show that in both the infinite and finite rectangular traps the dipole orientation aligns with the short cross-sectional axis over a finite range of aspect ratios. The equilibrium orientation in both cases is described by a phase diagram whose degeneracies we classify. Every prediction is verified against finite-element solutions of the same boundary-value problem, with agreement better than $0.16\%$.

Mapping a chemical reaction network, the graph of minima and transition states (TS) and the elementary reactions connecting them, is the natural language of chemistry, from catalysis to combustion to the origin of life. Constructing such a reaction network for a given chemistry has been impractical: it requires finding and characterizing tens of thousands of TS, a task for which traditional methods such as density functional theory (DFT) are typically prohibitively slow and require reactant and product as input. We introduce ReactionAtlas, which builds a reaction network $\textit{ab origine}$ from a handful of seed molecules and without hand-crafted rules. Specifically, our machine-learned generative model proposes reactions from kinetically sampled candidate compounds and a DFT-trained machine learned force field (MLFF) filters them to valid TS, the resulting products of which enter the search as new seeds. Starting from eight pre-biotic seeds (CH$_2$O, H$_2$O, OH$^-$, H$_3$O$^+$, CO$_2$, H$_2$CO$_3$, HCO$_3^-$, H), ReactionAtlas discovers $\sim$47,000 reactions among $\sim$12,000 compounds. The MLFF TSs match the PBE0 references within 0.5 Å RMSD in 85% of the cases and can be easily brought to the PBE0 level. Thus, ReactionAtlas maps small carbohydrate chemistry up to C$_4$H$_8$O$_4$ at unprecedented scale and accuracy, including charge and stereo information. It enables novel insights into many well-studied reaction paths, including the formose cycle, which we highlight for its centrality to the chemical origins of life. Notably, our framework also allows establishing alternative reaction pathways for formose chemistry.

Real-time feedback control of quantum systems is often limited by partial observations, nonlinear dynamics and measurement noise, which make accurate model-based controllers difficult to design. Here we show that deep reinforcement learning can cool the motion of a single neutral atom coupled to a high-finesse optical cavity using only the continuously monitored cavity transmission. We first train the controller in simulation and then transfer it to the experiment, where online fine-tuning adapts it to unmodeled experimental dynamics. The learned policy damps the atom's motion in real time and achieves a cooling time constant of 388 +/- 14 microseconds, corresponding to only two motional periods in the trap. It also outperforms a standard linear differentiator controller in cooling speed while maintaining comparable atom retention over a broad range of operating conditions. These results establish reinforcement learning as a practical strategy for feedback control in quantum-limited experiments where compact analytical models are incomplete.

Theta Eridani is a V=2.9 star that was nonetheless reported as one of the thirteen brightest stars in the night sky by both Ptolemy in his Almagest (137 AD) and by al-Sufi in his The Book of Fixed Stars (964 AD), in addition to being previously referred by Hipparchus (129 BC) as a particularly bright star. The discrepancy between its historical and modern visual magnitude $\Delta V \sim 2.7$ is the highest among the $\sim 1000$ stars in the Almagest. Theta Eridani is actually a triple star system, and here we combine interferometric data from VLTI/PIONIER and VLTI/GRAVITY, spectroscopic data from ESPaDOns and FEROS, and photometric data from TESS in order to solve for the orbital parameters, masses and radii of the close inner binary Theta Eridani Aa+Ab. We find that it is a tight eccentric binary ($a=0.083 \text{ au}$, $e=0.105$) of intermediate-mass stars ($M_{Aa}\simeq 2.3 M_{\odot}$, $M_{Ab}\simeq 2.2 M_{\odot}$) that are extended to $\sim 80\%$ of their Roche lobe radii ($R_{Aa}\simeq 4.3 R_{\odot}, R_{Ab} \simeq 4.0 R_{\odot}$), resulting in prominent ellipsoidal oscillations in the lightcurve. We also find that the primary is in a very special phase of its evolution in which it has just finished core hydrogen burning. The remarkable combination of orbital and stellar parameters hints that the historical brightening of Theta Eridani was due to a millenary transient phase powered by orbital energy extraction during a long-lived “common envelope” stage triggered by eccentric Roche lobe overflow in a previously more eccentric binary ($e\simeq0.6$). This strengthens the case that the apparent brightening was real and not due to an error by three different ancient observers, as has been commonly claimed in the past.

Globular clusters (GCs) are long time survivors of galaxy assembly and evolution yet their emergence from an initial cluster population is still poorly constrained. We present the Auriga GLOBular clustEr Simulations (AuriGLOBES) a physically motivated subgrid model for star cluster (SC) formation and evolution that includes enhanced mass loss from compact object remnants. With this model, implemented in the Auriga cosmological galaxy formation model, we run a suite of zoom-in cosmological simulations comprising 9 Milky Way mass and 5 lower mass galaxies. We demonstrate that our model produces plausible GC populations compared to the Milky Way/M31 systems and reproduces the empirical GC system mass – halo mass relation within a 2$\sigma$ scatter. We show that the formation of SCs in tidally compressive, high-pressure gas in addition to enhanced mass loss from compact object remnants heating is required to capture the transformation of an initial Schechter mass function to the characteristic observed GC mass function in the Milky Way/M31 systems. The resulting GC populations show spatial and metallicity distributions qualitatively similar to the Milky Way/M31 systems, as well as a variety of age distributions that correlate with the star formation history of the simulated galaxies. However, the peak of the age distribution of Milky Way GCs is older than any of our simulated Milky Way-mass galaxies, which is attributed to unrepresented star formation and galaxy assembly histories. AuriGLOBES represents a reliable framework for the study of GC populations through cosmic history and a robust foundation for future applications for a model of stellar streams arising from GCs disruption.

Photonic waveguide design often requires repeated full-vector Maxwell eigenmode solves over wavelength, geometry, and material parameters. We present an adaptive eigenvector-continuation framework for accelerating and stabilizing these modal sweeps. The method constructs a reduced basis from selected full-order modal snapshots, solves projected Maxwell eigenproblems at new query points, reconstructs the modal fields, and monitors accuracy with a full operator residual. We demonstrate three regimes. In fixed-geometry wavelength sweeps of a strip waveguide, well-distributed snapshots reproduce the target modal branch with low residual and low effective-index error. In a multimode ridge waveguide, a shared reduced basis containing several modal families enables robust broadband mode-family tracking and residual-guided adaptive enrichment. In geometry-dependent width sweeps, the method gives accurate effective-index predictions and high field overlap, but the residual reveals moving-boundary errors caused by non-smooth changes of the discrete operator on a fixed Cartesian grid. These results show that adaptive eigenvector continuation is an operator-consistent modal emulator and diagnostic tool for photonic waveguide sweeps.

We present LinApart3, an efficient multivariate partial fraction decomposition algorithm for rational functions with linear denominators. Our decomposition algorithm guarantees that each term contains at most as many distinct denominators from the original set as partial fraction variables, introduces no spurious singularities, is independent of variable ordering, and is insensitive to the presence of spectator variables. While general multivariate approaches based on Gröbner bases or Leinartas' method handle arbitrary polynomial denominators, they suffer from intermediate expression swell. LinApart3 replaces polynomial-ideal computations with linear algebra and residue extraction by exploiting the geometry of the hyperplane arrangement defined by the denominators, circumventing this issue just as LinApart did in the univariate case. Because the individual basis contributions are independent, the algorithm is moreover naturally parallelizable. To showcase the utility of our algorithm we implemented the algorithm both in Wolfram Mathematica and FORM.

Discovering governing equations directly from observational data is a key step towards interpretable scientific machine learning. Current data-driven approaches typically operate on a single dataset, inherently limiting their performance when faced with restricted observations. In practice, multiple datasets are often available for the same physical system, distinguished only by distinct initial conditions or boundary configurations. Here, we present a competitive optimization framework designed to discover shared partial differential equations (PDEs) from multi-source datasets, termed MCO-PDE. The framework first trains independent neural surrogates for each data source, and then employs a soft-competitive weighting mechanism to dynamically assess dataset credibility and aggregate a consensus global coefficient. Integrated with a genetic algorithm for structural search, this approach simultaneously identifies the functional forms and parameters of the governing laws. We demonstrate that fusing as few as 50 observations per dataset across seven cases recovers canonical equations with high accuracy. The framework inherently handles two- and three-dimensional domains characterized by irregular boundaries and heterogeneous coefficients, and successfully extracts physically meaningful laws from real-world wave-tank experiments. Overall, this work establishes a promising route for automated scientific discovery via heterogeneous data fusion.

Non-Clifford gates are a necessary resource for universal quantum computation, yet their fault-tolerant implementation typically relies on magic-state distillation, which incurs significant overhead in qubit count and circuit depth. In this work, we propose a probabilistic cavity-based magic-state injection protocol. Our scheme exploits controlled atom-cavity interactions and conditional measurements to probabilistically prepare an effective magic state encoded in the first two level Fock subspace of a single cavity mode, achieving a success probability of $0.74$ per attempt, independent of the target magic phase. The cavity-encoded magic state is subsequently injected into a computational atom via a teleportation-based protocol mediated by dressed-state transitions, requiring only Clifford operations and a single auxiliary atom for readout. We show that all required operations – state preparation, two-qubit exchange gates, and projective measurement – can be implemented with experimentally available techniques in Rydberg atom-cavity platforms. We further discuss how the scheme can in principle be adapted to operate at the logical level, where collective Rydberg interactions and optical nonlinearities provide a route toward cavity-mediated $T$-gate injection directly into code-encoded qubits.

We present a novel {\delta}f particle-in-cell (PIC) method for the kinetic simulation of electrostatic plasmas in which the bulk density, acting as a control variate, is evolved using symplectic neural networks (SympNets). The SympNets are used as an approximation of the backward flow and trained using the particle trajectories. We introduce a periodic variant of the SympNet architecture that encodes the spatial periodicity of the problem into the network itself. We validate the approach with numerical results in 1D1V and 3D3V for the Vlasov-Poisson system.

Decisions to migrate depend on others' decisions. Dependence can produce nontrivial dynamics. We propose a minimal migration model that accounts for social influence alongside individual heterogeneity in mobility as migrants move from region to region. In special locations of parameter space, migrant flows dramatically and spontaneously fluctuate. Such aspects mimic observed fluctuations in migration statistics and thus show how large fluctuations in data can reflect more than response to events like armed conflict and natural disasters. Correspondingly, the impact of exogenous factors can be confounded with the results of collective decisions.

We describe a fabrication process for preparing liquid suspensions of micron-scale Au and Au/graphene bilayer platelets using thin-film deposition, optical lithography, ion milling, hydrofluoric acid (HF) substrate etching, and release from the substrate into a liquid suspension. Residual HF is removed through repeated centrifugation, decanting, and dilution cycles. The resulting suspension is characterized by electrospray deposition onto a secondary substrate, followed by electron and atomic force microscopy. The deposited platelets exhibit minimal aggregation, and the overall platelet yield reaches up to 30% of the platelets originally patterned on the wafer. Lateral force microscopy further confirms that the Au/graphene bilayer remains intact throughout fabrication, release, and electrospray deposition. This process provides a practical route for preparing high-quality platelet suspensions for levitated nanoparticle experiments and other applications requiring suspensions of two-dimensional nanostructures.

Recent experiments on molecular droplets have opened a new frontier of self-organization in strongly dipolar quantum matter. Microwave-dressing of polar molecules permits to tune both the strength and the angular structure of long-range interactions, potentially promoting a rich spectrum of quantum phases, from superfluid droplets with varying geometry and insulating or supersolid droplet arrays to strongly correlated crystals of individual molecules. Using path-integral Monte Carlo simulations of large molecular ensembles, we demonstrate that experimentally observed droplet arrays emerge as a metastable non-equilibrium state from the quenching of a gas-droplet phase transition under entirely broken rotational symmetry of the microwave-induced interaction potential. We moreover find that a crystalline phase of molecules, predicted for antidipolar interactions, is absent under conditions of recent experiments. This is traced back to the lack of angular symmetry in currently employed microwave-dressing, which qualitatively reshapes the many-body energy landscape and cannot be captured by effective scalar interaction parameters. Our results provide the first direct comparison of ab initio simulations and experiments and establish interaction anisotropy as a key aspect of molecular quantum gases.

Excited-state dynamics of open quantum systems is analyzed by the hierarchical equations of motion (HEOM) or the thermalized time-evolving density operator with orthogonal polynomials algorithm (T-TEDOPA) method when a discrete $ab$ $initio$ linear vibronic model is parametrized by continuous temperature-dependent spectral densities leading to crossed correlation functions, i.e. correlated fluctuations of the energy gap collective modes. We focus on a conical intersection involving two collective modes tuning the energy of each excited state and we revisit the transformation of the initial correlated tuning baths to de-correlated shared baths in order to reduce the computational resources. While a completely frequency-dependent transformation poses problems for HEOM, we find that in some particular cases, an optimal approximate frequency-independent transformation may be derived. On the contrary, T-TEDOPA is very efficient and allows to use this frequency-dependent transformation at the price of managing long-range couplings in the tensor chain. An illustrative application is shown by using the linear vibronic coupling model of a planar symmetrical (phenylethynyl)benzene dimer.

Calculation of binding energies for protein-ligand molecular systems requires accurate treatment of the electronic structure, a quantum chemistry problem that scales exponentially on classical hardware, while current quantum hardware remains too noisy for the required circuit depths. This report presents a hybrid quantum-classical workflow performed on the Fujitsu FX700 ideal state-vector simulator using QARP that addresses two structural inefficiencies in quantum-sampling-based diagonalization workflows. First, we integrate the Linear Scaling CNOT UCCSD (LCNot-UCCSD) ansatz into the QSCI framework, replacing the $\mathcal{O}(N^6)$ CCSD parameter initialization of the competing LUCJ ansatz approach with $\mathcal{O}(N^4)$ MP2-amplitude initialization. Second, we introduce QSCI-RBM, a variant that replaces the configuration recovery of the SQD framework with a Restricted Boltzmann Machine (RBM) acting as a compact generative subspace expansion model. Both are evaluated on eight different molecules in STO-3G across 14 controlled artificial error levels with 100 independent runs each, validated on potential energy surface scans of the N$_2$ molecule in cc-pVDZ, and embedded within DMET to treat the FDA-approved antiviral Amantadine (C$_{10}$H$_{17}$N, 11 DMET fragments) and the active region of the SARS-CoV-2 main protease complexed with its covalent inhibitor Carmofur (PDB: 7BUY, C$_{15}$H$_{28}$N$_4$O$_5$S, 10 fragments). To our knowledge, this is the first deployment of LCNot-UCCSD within QSCI on a quantum computing simulator, and the first DMET-QSCI(LCNot-UCCSD)-RBM application to an industry-relevant protein-ligand system. By utilizing a fraction of the classical computing resources required by the current state-of-the-art work by Cleveland Clinic, RIKEN, and IBM Quantum, this approach enables more efficient and economical drug discovery simulations for the industry.

Stars like our Sun expand as they exhaust their core hydrogen fuel, becoming red giants that eventually reach sizes up to 100 times their original radius. These giants have long presented a puzzle: they show systematic changes in their surface chemical composition that can only be explained by the transport of material from their nuclear-burning interior to their surface. The challenge is that this transport must somehow cross a stable layer that acts as a barrier between the star's outer convective envelope and its nuclear-burning interior. The convective motions in the envelope create internal waves that propagate through this barrier layer, but on their own these waves produce very little material transport. Here we show through high-resolution three-dimensional hydrodynamical simulations that stellar rotation dramatically amplifies how effectively these waves can mix material across this barrier. We find that the mixing rates can exceed those in non-rotating stars by over 100 times, increasing with faster rotation rates. This enhanced mixing provides a natural explanation for the observed chemical signatures in typical red giants. The amplification of wave-driven mixing by rotation may have implications beyond red giants to other types of stars.

Physical heterogeneities in the context of sliding friction, such as a human finger exploring an object, have been well studied, yet the behavior of chemical heterogeneities in mesoscale soft sliding remains underexplored, despite the similar prevalence of chemical and physical variations in real systems. Here, we experimentally characterized the friction of a planar soft elastic probe sliding across a single chemical heterogeneity that was formed at the interface of two silanes on silicon wafers. By constructing phase maps across multiple loads and velocities, we quantified the occurrence of several frictional phenomena at and around the chemical edge, including stiction spike formation, edge slope direction, baseline shifts, and baseline drift, and quantified their sliding direction-dependent formation. We found that chemical heterogeneities made by more disparate materials (butyl- and aminopropyl-terminated) exhibited several phenomena that were more often direction-independent compared to chemical heterogeneities formed from more similar materials (butyl- and hexyl-terminated). We attributed this directional asymmetry to elastic body effects. In subsequent human testing (n=36), we observed that humans also exhibited directional-dependent accuracy (66.7% versus 38.9%) on one pair (butyl- and hexyl-terminated) but not the other (77.8% versus 75%), which in the context of our phase maps, suggests that the slope of the friction force when sliding over a chemical edge is important for generating a clear edge of a tactile feature, rather than the differences in simple material properties or other friction phenomena.

We show convergence of solutions to a Navier–Stokes/Allen–Cahn system as the interfacial thickness $\varepsilon>0$ tends to zero for well-prepared initial data as long as the limit system possesses a sufficiently smooth solution. The limit system consists of a two-phase Navier–Stokes system separated by a sharp interface in the presence of surface tension coupled to a convective mean curvature flow equation. In comparison to previous results we obtain improved convergence estimates for higher-order norms. These enable us to prove convergence in the case of three space dimensions and non-constant viscosity, which was unknown before. The convergence results relies crucially on uniform higher-order estimates for the associated linearized Navier–Stokes/Allen–Cahn system in suitably weighted $L^2$-Sobolev spaces. Here a novel problem-adapted weight proportional to the sum of $\varepsilon$ and the distance to the sharp interface of the limit, which gives improved and sharp estimates, is an important new ingredient. This approach can be potentially adapted to other sharp interface limits as well.

We develop a theory for the electrophoresis of a spherical porous liquid droplet with prescribed uniform surface charge. The exterior electrokinetics is governed by the Poisson-Nernst-Planck-Stokes equations, while the internal liquid motion is described by the Brinkman-Debye-Bueche equation. A regular perturbation expansion in the applied electric field reduces the governing equations to coupled radial ordinary differential equations. In the Debye-Hückel regime, we derive a closed-form mobility expression valid for arbitrary Debye layer thickness. The analysis shows that the porous interior modifies clean-droplet electrophoresis through a single Brinkman-screened hydrodynamic resistance, yielding a continuous transition between clean-droplet and rigid-particle limits. Numerical solutions beyond the low-potential regime reveal a non-universal role of permeability: increasing the Darcy number can either suppress or enhance the mobility. This reversal is determined by the sign of the interfacial-velocity mode, which is governed by the competition between tangential Maxwell traction and hydrodynamic shear generated by electric-double-layer distortion. Dielectric polarization, surface charge and double-layer thickness can reverse the internal circulation, while the Darcy number controls how strongly this circulation is transmitted through the porous interior. This permeability sensitivity is especially pronounced for highly polarizable droplets in the thin-double-layer regime. The theory provides a basis for tuning electrokinetic transport of soft porous droplets in microfluidic and biomedical technologies.

Nonlinear photonic time crystals (PTCs) can support solitons inside momentum k gaps, where the amplification of k gap modes is saturated by Kerr nonlinearity, forming spatially homogeneous but temporally localized excitations. Yet their quantum nature remains unclear. Here we quantize nonlinear k gap dynamics of PTCs and show that k gap solitons are represented by biphoton Fock ladder states. K gap amplification drives two-mode squeezing of the biphoton, while Kerr nonlinearity generates an anharmonic potential along the biphoton Fock ladder that balances this squeezing process, creating a finite biphoton number turning point and giving rise to quantum collapse and revival dynamics and nonclassical phase space interference. We further analyze how photon loss and dephasing reshape the biphoton statistics of quantized k gap solitons. Our results establish a biphoton Fock space description of k gap soliton quantization and provide a framework for studying quantum nonlinear excitations and entangled light generation in photonic time crystals.

Herein it is shown spontaneous symmetry breaking in supramolecular aggregates of D4h-symmetric zinc phthalocyanine (ZnPc). Ab initio density functional theory calculations at the M06/DGDZVP level reveal that intermolecular interactions induce a subtle relaxation of the macrocyclic framework, producing a characteristic red shift of the aza-bridge (C-N-C) stretching mode and symmetric stretching of the pyrrole ring. Confocal Raman Optical Activity measurements further reveal a negative Cotton effect at 1505 cm^-1 and 1338 cm^-1, providing evidence of emergent supramolecular chirality. Our findings identify pristine ZnPc as a model system for spontaneous self-assembled chiral symmetry breaking in molecular condensates and suggest new opportunities for chiroptical, spin-selective, and quantum functional materials.

Normalizing Flows excel at modeling a single fixed density, yet many problems across the sciences, such as high energy physics, instead require modeling how that density deforms as a function of continuous parameters: the strength of a physical effect, a calibration constant, or a source of systematic uncertainty. Learning a separate flow for every parameter configuration quickly becomes intractable, since the number of joint settings grows exponentially with the number of parameters. We introduce Factorizable Normalizing Flows (FNFs), which represent the parameter-dependent density as a fixed, high-fidelity flow for a reference configuration composed with a learnable transformation that is polynomial in the parameters and factorized over them. This structure has a practical consequence: each parameter's effect is learned in isolation, from samples in which that parameter alone is varied. The combined response of many parameters is then recovered by summation at inference, without ever sampling their combinatorially large joint space. On a controlled problem with two interpretable deformations applied jointly to the data, the learned transformation reproduces the true deformations and matches the optimal likelihood, while optional interaction terms capture residual correlations when several parameters vary strongly at once. The resulting model is interpretable, scales linearly with the number of parameters, and keeps the likelihood tractable. This provides a general tool for any inference workflow requiring continuous density morphing, and directly enables the next generation of unbinned likelihood fits in high energy physics.

Hit-level ML-based particle reconstruction methods have recently shown promising results. However, the reconstruction models are currently provided with targets that are unaware of the detector geometry and its resolution, resulting in training ambiguities. This can introduce a dependence on sample priors and reduce robustness under changes in event topology. We study the effect of a detector-aware target definition in the context of end-to-end Particle Flow reconstruction using a generic GEANT4-based detector simulation. We introduce the concept of detector-aware targets built from calorimeter showers with a hit-based merging algorithm based on cell-wise energy sharing that takes into account the spatial resolution of the detector. This includes a Particle-Flow-aware variant that preserves charged-particle consistency. Using a fixed GNN-based reconstruction model, we show that merged targets improve physics performance on a training-like sample. More importantly, models evaluated on an independent sample with different particle composition and topology show improved momentum response and resolution when trained with PF-aware merged targets. Our results show that removing experimentally non-resolvable target structure enhances not only reconstruction performance, but also improves model robustness against process-dependent variations in event topology.